Artificial Intelligence is the New Electricity — Andrew Ng

Abstract

On Wednesday, January 25, Andrew Ng – former Baidu Chief Scientist, Coursera co-founder, and Stanford Adjunct Professor – gave a talk at the Stanford MSx Future Forum. During the talk, Professor Ng shared his opinion on AI. He mainly discussed how artificial intelligence (AI) is transforming industry and business.

Impact of AI on business

The process of working with AI and how AI affects specifically products

About a century go, we started to electrify the world through the electrical revolution. By replacing steam powered machines with those using electricity, we transformed transportation, manufacturing, agriculture, healthcare and so on. Now, AI is poised to start an equally large transformation on many industries. For example, the IT industry (like Baidu) is totally transformed by AI. FinTech is being also being totally transformed. Healthcare is starting to be, and there is huge opportunities there. Self-driving is an industry built on AI. Others industries, like search engines and food delivery, are also supported by AI. The only industry which will not be transformed will probably be hairdressing.

What is AI doing? What can AI really do?

AI is driving tremendous economic value, easily in the billions. And this is only by one type of AI – one idea. The technical term is called Supervised Learning, which uses AI to figure out a relatively simple A to B mapping, or A to B response. For example, a system determining whether an email is spam or not, or determining the objects in an image. Another example would be a system that takes in an audio clip and outputs a transcript of what was said.

Although it can automatically perform many functions, today’s AI is still extremely limited compared to human intelligence. “Anything that a typical human can do with at most 1 sec of thought, can probably now or soon be automated with AI.” This is an imperfect rule but quite helpful.

Over time, AI will tend to make rapid progress up until reaching human level performance. When AI surpasses human performance, the progress will slow down due to several reasons. First is the feasibility of the things human can do. The second reason is the sheer size of data. And the third reason is a distinctively human ability called “insights”.

AI replacing human

The downside to the progress in AI is the implication to the job market. If AI is especially good at doing whatever humans can do, then AI software will be in direct competition with people for a lot of jobs. It is already beginning to show signs of this now, but will be even more common in the future.

The major trends of AI

AI have been around for several decades, but it’s only in the last five years that AI has really taken off. Why?

Several years ago, researchers were using earlier generations of AI software and earlier generations of machine learning algorithms. Even when provided with more data, the performance of these systems did not keep on getting better. However, because of Moore’s Law and the utilization of GPUs, even if a Neural Network is fed a small amount of data, its performance far surpasses that of traditional algorithms. Now, the leading edge in AI research is shifting to supercomputers, or HPCs (High Performance Computing).

Neural Network

The magic thing about Neural Networks is that you do not need to worry about the output of intermediate layers, because it can figure them out by itself. Part of the magic is when you have enough data, it can figure out a lot of things by itself. All the smarts in a neural network comes from us giving it tons of data.

How to build a defensible business in AI?

Today, the AI research community is quite open. Almost all of the leading research groups tend to publish their results freely and openly. It’s difficult to keep algorithms secret. So how to build a defensible business using AI? There are two scarce resources. One is data: it’s actually very difficult to acquire huge amounts of data. Baidu has 50,000 hours of training data for speech recognition, and is expected to train about another 100,000 hours of data. That’s over 10 years of audio data. For facial recognition, the largest academic papers in computer vision is published on maybe 15 million images. However, at Baidu, 200 million images were trained. Large companies usually launch products not for revenue but for data.

The other and the most scarce resource is talent, because AI needs to be customized for the business context. You can’t just download an open source package and apply it to your problem.

Virtuous and non-virtuous circles of AI

Figure 3: The virtuous circle of AI.

Product → Users → Data → Product. The best products have the most users, and the most users usually means getting the most data, and with modern ML, the product becomes better.

Figure 4: The non-virtuous circle of AI.

Andrew is not happy about it for the reason that there is no clear path to how AI can become sentient. Worrying about evil AI killing humans is a little bit like worrying about overpopulation on the planet Mars.

AI product management

Product managers do a lot of work. They go out and try to identify what’s important to users, and come up with ideas in their head on what this product should be. But how do they communicate that to the engineers? The answer is through data, which means the PM be responsible for coming up with a data set. This is one of the most effective processes for letting the PM specify what they really care about.

Figure 5: Communication between PM and engineer is necessary and important.

Some opportunities of AI

Some things are coming in the very near future. First, speech recognition will take off. It’s just in the last two years that speech recognition reached the level of accuracy where it is becoming incredibly useful. And five months ago, there was a Standford University led study done by computer science professor James Landay, where he showed that it is 3 times faster to use speech recognition than typing on a cellphone.

Secondly, computer vision will take off a bit later. Facial recognition has made financial transaction and identification much easier and safer. You don’t need an ID card when you are in Baidu’s headquarters.

Then there’s AI in healthcare. A lot of radiologists graduating today will definitely be impacted by AI. It would not be a good plan to apply for a 40-year career in radiology because of the fast growing capabilities of AI.

What’s more, education will also be changed in the near future by AI.

The last issue is on jobs. AI has displaced many jobs, but it will also create new ones. A new educational system is needed for people whose jobs are displaced in order to retrain them to take on the new jobs.

AI has had several winters before, but it has passed into the phase of eternal spring.